•A stochastic model for the optimal operation of virtual power plants is developed.•The scenario tree contains both market and renewable generation scenarios.•An ad-hoc decomposition method ...drastically reduces the computational time.•Despite optimal solution, PV generation is partially curtailed.•Cogeneration plant can effectively participate in more markets.
As more uncontrollable renewable energy sources are present in the power generation portfolio, the need of more detailed and reliable tools for the optimal operation of energy systems has increased in the last years. This work presents a multi-stage stochastic Mixed Integer Linear Program with binary recourse for optimizing the day-ahead unit commitment of power plants and virtual power plants operating in the day-ahead and ancillary services markets. Scenarios reproduce the uncertainty of the ancillary services market requests, and production of photovoltaic panels. A novel decomposition algorithm is proposed to tackle the challenging multistage stochastic program. The methodology is tested on three types of large power plants: a natural gas-fired combined cycle, a combined heat and power combined cycle with thermal storage, and a virtual power plant integrating a combined cycle with battery and photovoltaic fields. Compared to the typical deterministic unit commitment approach, the proposed stochastic optimization approach allows to increase the revenues of the conventional power plant up to 13.58% and, for the combined heat and power and virtual power plant case, it allows finding a feasible and efficient operational scheduling.
This paper presents the optimization of organic Rankine cycles (ORCs) for recovering waste heat from a hypothetical aluminum production plant to be installed in Norway. The case study is particularly ...interesting because it features two hot streams at different temperatures (the pot exhaust gases and the cell wall cooling air), which make available about 16 MWth below 250°C. First, a recently proposed cycle optimization approach is adopted to identify the most promising working fluid and optimize the cycle variables (pressures, temperatures, mass flow rates) for the maximum energy performance. The analysis includes both pure fluids, including recently synthesized refrigerants, and binary zeotropic mixtures assessing in total 102 working fluids. The best pure fluid in terms of exergy efficiency turns out to be HFE-347mcc (which can achieve a target exergy efficiency of 85.28%), followed by neopentane, butane, and R114. HFO-1336mzz appears to be one of the most promising non-flammable alternatives with low Global Warming Potential (GWP). The mixture leading to the highest exergy efficiency is isobutane–isopentane, which can increase the net electrical power output by up to 3.3% compared to pure fluids. The systematic techno-economic optimization, repeated for two different electricity prices, shows that RE347mcc is the best option in both low and high electricity prices. The cost of the cycle using HFO-1336mzz is penalized by the larger evaporation heat (negatively influencing the heat integration) and the smaller regenerator.
This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1)-reliability requirements. The problem is formulated ...as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs.
•N-1 reliability is included in MILP for the optimal design of Aggregated Energy Systems.•Different approaches to include reliability constraints are proposed and compared.•Two decomposition algorithms allow reducing the computational time considerably.•Including N-1 reliability results in a 15–20% Total Annual Cost increase.
•Rolling horizon algorithm for the seasonal and day-ahead operational optimization.•Affine Adjustable Robust Optimization MILP optimizes day-ahead and correction rules.•Long-term targets allow ...managing seasonal storages and yearly constraints.•Tests show that the optimized solutions are always feasible.•The operational cost of the robust solutions is 6–22% higher than the ideal solution.
This work proposes an approach for the robust operational optimization of Aggregated Energy Systems (AES) on three key time scales: seasonal, day-ahead and real-time hourly operation. The evaluation of all the three time-scales is fundamental for AESs featuring seasonal storage systems and/or units (such as combined heat and power plants) with yearly-basis constraints on relevant performance indexes or emissions. The approach consists in a rolling horizon algorithm based on an Affine Adjustable Robust Optimization model for optimizing both day ahead schedule (commitment and economic dispatch) and the decision rules to adjust the real-time operation. The robust optimization model takes as input (i) the day-ahead forecasts of renewable production and energy demands with their corresponding uncertainty, (ii) past and future expected performance of the units with yearly constraints, and (iii) target end-of-the-day charge levels for the seasonal storage system. These long-term targets are estimated by optimizing the operation over representative years defined on the basis of the past measured data. The proposed methodology is tested on three real-world case studies, featuring up to four short-term uncertain parameters (energy demands and non-dispatchable generation), yearly constraints and seasonal storage. Results shows that the proposed methodology meets the yearly constraints and safely manages the seasonal storage without shortages, while always meeting the energy demands (no shedding). In addition, the cost of short- and long-term uncertainty were evaluated by comparing the results of the robust rolling horizon with other two deterministic approaches, proving a limited increase.
Background:
Recent in-vitro data have shown that the activity of monoclonal antibodies (mAbs) targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) varies according to the variant of ...concern (VOC). No studies have compared the clinical efficacy of different mAbs against Omicron VOC.
Methods:
The MANTICO trial is a non-inferiority randomised controlled trial comparing the clinical efficacy of early treatments with bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab in outpatients aged 50 or older with mild-to-moderate SARS-CoV-2 infection. As the patient enrolment was interrupted for possible futility after the onset of the Omicron wave, the analysis was performed according to the SARS-CoV-2 VOC. The primary outcome was coronavirus disease 2019 (COVID-19) progression (hospitalisation, need of supplemental oxygen therapy, or death through day 14). Secondary outcomes included the time to symptom resolution, assessed using the product-limit method. Kaplan-Meier estimator and Cox proportional hazard model were used to assess the association with predictors. Log rank test was used to compare survival functions.
Results:
Overall, 319 patients were included. Among 141 patients infected with Delta, no COVID-19 progression was recorded, and the time to symptom resolution did not differ significantly between treatment groups (Log-rank Chi-square 0.22, p 0.90). Among 170 patients infected with Omicron (80.6% BA.1 and 19.4% BA.1.1), two COVID-19 progressions were recorded, both in the bamlanivimab/etesevimab group, and the median time to symptom resolution was 5 days shorter in the sotrovimab group compared with the bamlanivimab/etesevimab and casirivimab/imdevimab groups (HR 0.53 and HR 0.45, 95% CI 0.36–0.77 and 95% CI 0.30–0.67, p<0.01).
Conclusions:
Our data suggest that, among adult outpatients with mild-to-moderate SARS-CoV-2 infection due to Omicron BA.1 and BA.1.1, early treatment with sotrovimab reduces the time to recovery compared with casirivimab/imdevimab and bamlanivimab/etesevimab. In the same population, early treatment with casirivimab/imdevimab may maintain a role in preventing COVID-19 progression. The generalisability of trial results is substantially limited by the early discontinuation of the trial and firm conclusions cannot be drawn.
Funding:
This trial was funded by the Italian Medicines Agency (Agenzia Italiana del Farmaco, AIFA). The VOC identification was funded by the ORCHESTRA (Connecting European Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 101016167.
Clinical trial number:
NCT05205759.
Coronavirus disease 2019 (COVID‐19) infection has the potential for targeting the central nervous system, and several neurological symptoms have been described in patients with severe respiratory ...distress. Here, we described the case of a 60‐year‐old patient with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection but only mild respiratory abnormalities who developed an akinetic mutism attributable to encephalitis. Magnetic resonance imaging was negative, whereas electroencephalography showed generalized theta slowing. Cerebrospinal fluid analyses during the acute stage were negative for SARS‐CoV‐2, positive for pleocytosis and hyperproteinorrachia, and showed increased interleukin‐8 and tumor necrosis factor‐α concentrations. Other infectious or autoimmune disorders were excluded. A progressive clinical improvement along with a reduction of cerebrospinal fluid parameters was observed after high‐dose steroid treatment, thus arguing for an inflammatory‐mediated brain involvement related to COVID‐19. ANN NEUROL 2020;88:423–427.
Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson's disease (PD). However, this approach is ...burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered "optimum" in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.
Background
From February 21, the day of hospitalisation in ICU of the first diagnosed case of Covid-19, the social situation and the hospitals’ organisation throughout Italy dramatically changed.
...Methods
The CIO (Club Italiano dell’Osteosintesi) is an Italian society devoted to the study of traumatology that counts members spread in public and private hospitals throughout the country. Fifteen members of the CIO, Chairmen of 15 Orthopaedic and Trauma Units of level 1 or 2 trauma centres in Italy, have been involved in the study. They were asked to record data about surgical, outpatients clinics and ER activity from the 23rd of February to the 4th of April 2020. The data collected were compared with the data of the same timeframe of the previous year (2019).
Results
Comparing with last year, overall outpatient activity reduced up to 75%, overall Emergency Room (ER) trauma consultations up to 71%, elective surgical activity reduced up to 100% within two weeks and trauma surgery excluding femoral neck fractures up to 50%. The surgical treatment of femoral neck fractures showed a stable reduction from 15 to 20% without a significant variation during the timeframe.
Conclusions
Covid-19 outbreak showed a tremendous impact on all orthopaedic trauma activities throughout the country except for the surgical treatment of femoral neck fractures, which, although reduced, did not change in percentage within the analysed timeframe.
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